SNA Descritive Analysis from “Projeto Redes de Atenção às pessoas que consomem álcool e outras Drogas em Juiz de Fora-MG Brazil” - SNArRDJF
Here you can find a basic script to analysis data from SNArRDJF - this script was elaborated considering its use for orther matrix adjacency data from SNArRDJF - Here we are going to analyse:
########################## Basic Preparation ##### `#########################
rm(list = ls()) # removing previous objects to be sure that we don't have objects conflicts name
load("~/SNArRDJF/Robject/var2_data.RData")
suppressMessages(library(RColorBrewer))
#suppressMessages(library(car))
#suppressMessages(library(xtable))
suppressMessages(library(igraph))
#suppressMessages(library(miniCRAN))
#suppressMessages(library(magrittr))
#suppressMessages(library(keyplayer))
#suppressMessages(library(dplyr))
#suppressMessages(library(feather))
#suppressMessages(library(visNetwork))
#suppressMessages(library(knitr))
suppressMessages(library(DT))
#In order to get dinamic javascript object install those ones. If you get problems installing go to Stackoverflow.com and type your error to discover what to do. In some cases the libraries need to be intalled in outside R libs.
#devtools::install_github("wch/webshot")
#webshot::install_phantomjs()
set.seed(123)
#var2<-simplify(var2) #Simplify
• For undirected graphs:
– Actor centrality - involvement (connections) with other actors
• For directed graphs:
– Actor centrality - source of the ties (outgoing edges)
– Actor prestige - recipient of many ties (incoming edges)
In general - high centrality degree means direct contact with many other actors
V(var2)$indegree<-degree(var2, mode = "in") # Actor prestige - recipient of many ties (incoming edges)
V(var2)$outdegree <- degree(var2, mode = "out") # Actor centrality - source of the ties (outgoing edges)
V(var2)$totaldegree <- degree(var2, mode = "total")
var2_indegree<-degree(var2, mode = "in")
var2_outdegree<-degree(var2, mode = "out")
var2_totaldegree<-degree(var2, mode = "total")
##in
summary(var2_indegree)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 4.000 4.995 5.000 52.000
sd(var2_indegree)
## [1] 5.795342
hist(degree(var2, mode = "in", normalized = F), ylab="Frequency", xlab="Degree", breaks=vcount(var2)/10, main="Histogram of Indegree Nodes - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)")
##out
summary(var2_outdegree)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 4.995 4.000 92.000
sd(var2_outdegree)
## [1] 12.13256
hist(degree(var2, mode = "out", normalized = F), ylab="Frequency", xlab="Degree", breaks=vcount(var2)/10, main="Histogram of Outdegree Nodes - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)")
##all
summary(var2_totaldegree)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 5.000 9.989 9.000 144.000
sd(var2_totaldegree)
## [1] 16.54222
hist(degree(var2, mode = "all", normalized = F), ylab="Frequency", xlab="Degree", breaks=vcount(var2)/10, main="Histogram of All Degree Nodes - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)")
A slightly more nuanced metric is “strength centrality”, which is defined as the sum of the weights of all the connections for a given node. This is also sometimes called “weighted degree centrality”
V(var2)$var2_strength<- strength(var2, weights=E(var2)$weight)
var2_strength<- strength(var2, weights=E(var2)$weight)
summary(var2_strength)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 5.000 9.989 9.000 144.000
sd(var2_strength)
## [1] 16.54222
hist(strength(var2, weights=E(var2)$weight), ylab="Frequency", xlab="Degree", breaks=vcount(var2)/10, main="Histogram of Strength Degree Nodes - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)")
V(var2)$indegree_n<-degree(var2, mode = "in", normalized = T)
V(var2)$outdegree_n<- degree(var2, mode = "out", normalized = T)
V(var2)$totaldegree_n<- degree(var2, mode = "total", normalized = T)
var2_indegree_n<-degree(var2, mode = "in", normalized = T)
var2_outdegree_n<-degree(var2, mode = "out", normalized = T)
var2_totaldegree_n<-degree(var2, mode = "total", normalized = T)
summary(var2_indegree_n)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.01075 0.02151 0.02685 0.02688 0.27960
sd(var2_indegree_n)
## [1] 0.03115775
hist(degree(var2, mode = "in", normalized = T), ylab="Frequency", xlab="Normalized Degree", breaks=vcount(var2)/10, main="Histogram of Normalized Indegree Nodes - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)")
summary(var2_outdegree_n)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.005376 0.026850 0.021510 0.494600
sd(var2_outdegree_n)
## [1] 0.06522879
hist(degree(var2, mode = "out", normalized = T), ylab="Frequency", xlab="Normalized Degree", breaks=vcount(var2)/10, main="Histogram of Normalized Outdegree Nodes - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)")
summary(var2_totaldegree_n)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.01075 0.02688 0.05371 0.04839 0.77420
sd(var2_totaldegree_n)
## [1] 0.08893667
hist(degree(var2, mode = "all", normalized = T), ylab="Frequency", xlab="Normalized Degree", breaks=vcount(var2)/10, main="Histogram of Normalized All Degree Nodes - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)")
V(var2)$var2_centr_degree <- centralization.degree(var2)$res
var2_centr_degree <- centralization.degree(var2)
var2_centr_degree$centralization
## [1] 0.3621806
var2_centr_degree$theoretical_max
## [1] 69192
var2_degree.distribution<-degree.distribution(var2)
summary(var2_degree.distribution)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.000000 0.006897 0.005348 0.171100
sd(var2_degree.distribution)
## [1] 0.02156529
hist(degree.distribution(var2), breaks=vcount(var2)/10, ylab="Frequency", xlab="Degree Distribuition", main="Histogram of Degree Distribuition - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)")
dd <- degree.distribution(var2, cumulative=T, mode="all")
plot(dd, pch=19, cex=1, col="orange", xlab="Degree", ylab="Cumulative Frequency", main= "Cumulative Frequency of 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2) ")
dd.var2 <- degree.distribution(var2)
d <- 1:max(degree(var2))-1
ind <- (dd.var2 != 0)
plot(d[ind],
dd.var2[ind],
log="xy",
col="blue",
xlab=c("Log-Degree"),
ylab=c("Log-Intensity"),
main="Log-Log Degree Distribution For 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)"
)
The neighborhood of a given order y of a vertex v includes all vertices which are closer to v than the order. Ie. order y=0 is always v itself, order 1 is v plus its immediate neighbors, order 2 is order 1 plus the immediate neighbors of the vertices in order 1, etc.
var2_simplified<-simplify(var2)
var2_a.nn.deg <- graph.knn(var2_simplified, weights =E(var2_simplified)$weight)$knn %>% round(1)
V(var2_simplified)$var2_a.nn.deg <- graph.knn(var2_simplified, weights=E(var2_simplified)$weight)$knn
d<-cbind(V(var2_simplified)$LABEL_COR,var2_a.nn.deg)
datatable(d)
plot(degree(var2_simplified),
var2_a.nn.deg,
log="xy",
col="goldenrod",
xlab=c("Log Vertex Degree"),
ylab=c("Log Average Neighbor Degree"),
main="Average Neighbor Degree vs Vertex Degree - Log-Log Scale for 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)"
)
var2_a.nn.deg_w <- graph.knn(var2_simplified, weights=E(var2_simplified)$weight)$knn %>% round(1)
V(var2_simplified)$var2_a.nn.deg_w <-var2_a.nn.deg <- graph.knn(var2_simplified, weights=E(var2_simplified)$weight)$knn
summary(var2_a.nn.deg_w)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 2.00 29.80 38.30 49.72 74.80 144.00 6
sd(var2_a.nn.deg_w, na.rm = T)
## [1] 29.79951
d<-cbind(V(var2_simplified)$LABEL_COR,var2_a.nn.deg_w)
datatable(d)
plot(degree(var2_simplified),
var2_a.nn.deg,
log="xy",
col="goldenrod",
xlab=c("Log Vertex Degree"),
ylab=c("Log Average Neighbor Degree"),
main="Average Weighted Neighbor Degree vs Vertex Degree - Log-Log Scale For Weighted 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)"
)
var2_indegree<-degree(var2, mode = "in")
var2_outdegree<-degree(var2, mode = "out")
var2_totaldegree<-degree(var2, mode = "total")
var2_strength<- strength(var2, weights=E(var2)$weight)
var2_indegree_n<-degree(var2, mode = "in", normalized = T) %>% round(3)
var2_outdegree_n<-degree(var2, mode = "out", normalized = T) %>% round(3)
var2_totaldegree_n<-degree(var2, mode = "total", normalized = T) %>% round(3)
var2_centr_degree <- centralization.degree(var2)$res
var2_a.nn.deg <- graph.knn(var2_simplified)$knn %>% round(1)
var2_a.nn.deg_w <- graph.knn(var2_simplified, weights=E(var2_simplified)$weight)$knn %>% round(1)
var2_df_degree <- data.frame(var2_indegree,
var2_outdegree,
var2_totaldegree,
var2_indegree_n,
var2_outdegree_n,
var2_totaldegree_n,
var2_strength,
var2_centr_degree,
var2_a.nn.deg,
var2_a.nn.deg_w) %>% round(3)
#Adding type
var2_df_degree <-cbind(var2_df_degree, V(var2)$LABEL_COR)
#Adding names
names(var2_df_degree) <- c("In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree","Type")
#Ordering Variables
var2_df_degree<-var2_df_degree[c("Type","In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree")]
datatable(var2_df_degree, filter = 'top')
aggdata_mean <-aggregate(var2_df_degree, by=list(var2_df_degree$Type), FUN=mean, na.rm=TRUE)
#Removing Type variable
aggdata_mean<-aggdata_mean[,-c(2)]
names(aggdata_mean) <- c("Group", "In Degree(M)", "Out Degree(M)", "Total Degree(M)","In Degree Normalized(M)", "Out Degree Normalized(M)", "Total Degree Normalized(M)", "Strength(M)","Centralization Degree(M)","Average Neighbor Degree(M)","Average Weighted Neighbor Degree(M)")
aggdata_sd <-aggregate(var2_df_degree, by=list(var2_df_degree$Type), FUN=sd, na.rm=TRUE)
#Removing Type variable
aggdata_sd<-aggdata_sd[,-c(2)]
names(aggdata_sd) <- c("Group", "In Degree(SD)", "Out Degree(SD)", "Total Degree(SD)","In Degree Normalized(SD)", "Out Degree Normalized(SD)", "Total Degree Normalized(SD)", "Strength(SD)","Centralization Degree(SD)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(SD)")
total_table <- merge(aggdata_mean,aggdata_sd,by="Group")
#Rounding
Group<-total_table[,c(1)] #Keeping group
total_table<-total_table[,-c(1)] %>% round(2) #Rouding
total_table<-cbind(Group,total_table) #Binding toghter
#Organizing Variabels
total_table<-total_table[c("Group","In Degree(M)","In Degree(SD)", "Out Degree(M)", "Out Degree(SD)","Total Degree(M)", "Total Degree(SD)", "In Degree Normalized(M)", "In Degree Normalized(SD)", "Out Degree Normalized(M)", "Out Degree Normalized(SD)", "Total Degree Normalized(M)", "Total Degree Normalized(SD)", "Strength(M)","Strength(SD)", "Centralization Degree(M)","Centralization Degree(SD)","Average Neighbor Degree(M)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(M)", "Average Weighted Neighbor Degree(SD)")]
datatable(total_table, filter = 'top')
var2_df_degree <- data.frame(var2_indegree,
var2_outdegree,
var2_totaldegree,
var2_indegree_n,
var2_outdegree_n,
var2_totaldegree_n,
var2_strength,
var2_centr_degree,
var2_a.nn.deg,
var2_a.nn.deg_w) %>% round(3)
#Adding type
var2_df_degree <-cbind(var2_df_degree, V(var2)$TIPO1)
#Adding names
names(var2_df_degree) <- c("In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree","Type")
#Ordering Variables
var2_df_degree<-var2_df_degree[c("Type","In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree")]
datatable(var2_df_degree, filter = 'top')
aggdata_mean <-aggregate(var2_df_degree, by=list(var2_df_degree$Type), FUN=mean, na.rm=TRUE)
#Removing Type variable
aggdata_mean<-aggdata_mean[,-c(2)]
names(aggdata_mean) <- c("Group", "In Degree(M)", "Out Degree(M)", "Total Degree(M)","In Degree Normalized(M)", "Out Degree Normalized(M)", "Total Degree Normalized(M)", "Strength(M)","Centralization Degree(M)","Average Neighbor Degree(M)","Average Weighted Neighbor Degree(M)")
aggdata_sd <-aggregate(var2_df_degree, by=list(var2_df_degree$Type), FUN=sd, na.rm=TRUE)
#Removing Type variable
aggdata_sd<-aggdata_sd[,-c(2)]
names(aggdata_sd) <- c("Group", "In Degree(SD)", "Out Degree(SD)", "Total Degree(SD)","In Degree Normalized(SD)", "Out Degree Normalized(SD)", "Total Degree Normalized(SD)", "Strength(SD)","Centralization Degree(SD)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(SD)")
total_table <- merge(aggdata_mean,aggdata_sd,by="Group")
#Rounding
Group<-total_table[,c(1)] #Keeping group
total_table<-total_table[,-c(1)] %>% round(2) #Rouding
total_table<-cbind(Group,total_table) #Binding toghter
#Organizing Variabels
total_table<-total_table[c("Group","In Degree(M)","In Degree(SD)", "Out Degree(M)", "Out Degree(SD)","Total Degree(M)", "Total Degree(SD)", "In Degree Normalized(M)", "In Degree Normalized(SD)", "Out Degree Normalized(M)", "Out Degree Normalized(SD)", "Total Degree Normalized(M)", "Total Degree Normalized(SD)", "Strength(M)","Strength(SD)", "Centralization Degree(M)","Centralization Degree(SD)","Average Neighbor Degree(M)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(M)", "Average Weighted Neighbor Degree(SD)")]
datatable(total_table, filter = 'top')
var2_df_degree <- data.frame(var2_indegree,
var2_outdegree,
var2_totaldegree,
var2_indegree_n,
var2_outdegree_n,
var2_totaldegree_n,
var2_strength,
var2_centr_degree,
var2_a.nn.deg,
var2_a.nn.deg_w) %>% round(3)
#Adding type
var2_df_degree <-cbind(var2_df_degree, V(var2)$TIPO2)
#Adding names
names(var2_df_degree) <- c("In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree","Type")
#Ordering Variables
var2_df_degree<-var2_df_degree[c("Type","In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree")]
datatable(var2_df_degree, filter = 'top')
aggdata_mean <-aggregate(var2_df_degree, by=list(var2_df_degree$Type), FUN=mean, na.rm=TRUE)
#Removing Type variable
aggdata_mean<-aggdata_mean[,-c(2)]
names(aggdata_mean) <- c("Group", "In Degree(M)", "Out Degree(M)", "Total Degree(M)","In Degree Normalized(M)", "Out Degree Normalized(M)", "Total Degree Normalized(M)", "Strength(M)","Centralization Degree(M)","Average Neighbor Degree(M)","Average Weighted Neighbor Degree(M)")
aggdata_sd <-aggregate(var2_df_degree, by=list(var2_df_degree$Type), FUN=sd, na.rm=TRUE)
#Removing Type variable
aggdata_sd<-aggdata_sd[,-c(2)]
names(aggdata_sd) <- c("Group", "In Degree(SD)", "Out Degree(SD)", "Total Degree(SD)","In Degree Normalized(SD)", "Out Degree Normalized(SD)", "Total Degree Normalized(SD)", "Strength(SD)","Centralization Degree(SD)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(SD)")
total_table <- merge(aggdata_mean,aggdata_sd,by="Group")
#Rounding
Group<-total_table[,c(1)] #Keeping group
total_table<-total_table[,-c(1)] %>% round(2) #Rouding
total_table<-cbind(Group,total_table) #Binding toghter
#Organizing Variabels
total_table<-total_table[c("Group","In Degree(M)","In Degree(SD)", "Out Degree(M)", "Out Degree(SD)","Total Degree(M)", "Total Degree(SD)", "In Degree Normalized(M)", "In Degree Normalized(SD)", "Out Degree Normalized(M)", "Out Degree Normalized(SD)", "Total Degree Normalized(M)", "Total Degree Normalized(SD)", "Strength(M)","Strength(SD)", "Centralization Degree(M)","Centralization Degree(SD)","Average Neighbor Degree(M)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(M)", "Average Weighted Neighbor Degree(SD)")]
datatable(total_table, filter = 'top')
#Set Seed
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(var2, es=E(var2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var2))
maxC <- rep(Inf, vcount(var2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var2)$weight)
#PLotting
plot(var2,
layout=co,
edge.color=V(var2)$color[edge.start],
edge.arrow.size=(degree(var2)+1)/(30*mean(degree(var2))),
edge.width=E(var2)$weight/(10*mean(E(var2)$weight)),
edge.curved = TRUE,
vertex.size=log((degree(var2)+2))*(0.5*mean(degree(var2))),
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var2,"LABEL_COR"),
vertex.label.cex=log(degree(var2)+2)/mean(degree(var2)),
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var2)$LABEL_COR
b<-V(var2)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=2,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Vertex Degree Sized - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)", sub = "Source: from authors ", cex = .5)
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median In Degree: %.2f\n Median Out Degree: %.2f",
median(degree(var2, mode="in")),
median(degree(var2, mode="out"))
))
#Set Seed
set.seed(123)
#Get Variable
V(var2)$var2_color_degree<-V(var2)$totaldegree %>% round(0)
#Creating brewer pallette
vertex_var2_color_degree<-
colorRampPalette(brewer.pal(length(unique(
V(var2)$var2_color_degree)), "RdBu"))(
length(unique(V(var2)$var2_color_degree)))
#Saving as Vertex properties
V(var2)$vertex_var2_color_degree<- vertex_var2_color_degree[as.numeric(cut(degree(var2),breaks =length(unique(V(var2)$var2_color_degree))))]
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(var2, es=E(var2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var2))
maxC <- rep(Inf, vcount(var2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var2)$weight)
#PLotting
plot(var2,
layout=co,
#edge.color=V(var2)$color[edge.start],
edge.arrow.size=(degree(var2)+1)/1000,
edge.width=E(var2)$weight/10,
edge.curved = TRUE,
vertex.color=V(var2)$vertex_var2_color_degree,
vertex.size=log((degree(var2)+2))*10,
vertex.size=20,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var2,"LABEL_COR"),
vertex.label.cex=log((degree(var2)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var2)$var2_color_degree
b<-V(var2)$vertex_var2_color_degree
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
e<-e[order(e$a,decreasing=T),]
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=2,
bty="n",
ncol=1,
lty=1,
cex = .3)
#Adding Title
title("Network Vertex Degree Sized and Red to Blue - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median In Degree: %.2f\nMedian Out Degree: %.2f",
median(degree(var2, mode="in")),
median(degree(var2, mode="out"))
))
#Set Seed
set.seed(123)
#Get Variable
V(var2)$var2_color_degree<-V(var2)$var2_centr_degree
#Creating brewer pallette
vertex_var2_color_degree<-
colorRampPalette(brewer.pal(length(unique(
V(var2)$var2_color_degree)), "Spectral"))(
length(unique(V(var2)$var2_color_degree)))
#Saving as Vertex properties
V(var2)$vertex_var2_color_degree<- vertex_var2_color_degree[as.numeric(cut(V(var2)$var2_color_degree,breaks =length(unique(V(var2)$var2_color_degree))))]
#Plotting based only on degree measures
edge.start <- ends(var2, es=E(var2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var2))
maxC <- rep(Inf, vcount(var2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var2)$weight)
#PLotting
plot(var2,
layout=co,
edge.color=V(var2)$vertex_var2_color_degree[edge.start],
edge.arrow.size=(degree(var2)+1)/10000,
edge.width=E(var2)$weight/10,
edge.curved = TRUE,
vertex.color=V(var2)$vertex_var2_color_degree,
vertex.size=log((V(var2)$var2_centr_degree+2))*10,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var2,"LABEL_COR"),
vertex.label.cex=log((degree(var2)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var2)$var2_color_degree
b<-V(var2)$vertex_var2_color_degree
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
e<-e[order(e$a,decreasing=T),]
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=2,
bty="n",
ncol=1,
lty=1,
cex = .3)
#Adding Title
title("Network Vertex Centralization Degree Sized Spectral Colored - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median In Degree: %.2f\nMedian Out Degree: %.2f",
median(degree(var2, mode="in")),
median(degree(var2, mode="out"))
))
#Set Seed
set.seed(123)
# Network elements with lower than meadian degree
higherthanmedian.network_var2<-V(var2)[degree(var2)<median(degree(var2))]
#Deleting vertices based in intersection betewenn var2
high_var2<-delete.vertices(var2, higherthanmedian.network_var2)
#Plotting based only on degree measures
edge.start <- ends(high_var2, es=E(high_var2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(high_var2))
maxC <- rep(Inf, vcount(high_var2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(high_var2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(high_var2)$weight)
#PLotting
plot(high_var2,
layout=co,
edge.color=V(high_var2)$color[edge.start],
edge.arrow.size=(degree(high_var2)+1)/1000,
edge.width=E(high_var2)$weight/10,
edge.curved = TRUE,
vertex.size=log((V(high_var2)$var2_centr_degree+2))*10,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(high_var2,"LABEL_COR"),
vertex.label.cex=log((degree(high_var2)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(high_var2)$LABEL_COR
b<-V(high_var2)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=3,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Higher Than Median Degree - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Mean In Degree: %.2f\n Mean Out Degree: %.2f",
mean(degree(high_var2, mode="in")),
mean(degree(high_var2, mode="out"))
)
)
#Set Seed
set.seed(123)
# Network elements with lower than meadian degree
lowerthanmedian.network_var2<-V(var2)[degree(var2)>median(degree(var2))]
#Deleting vertices based in intersection betewenn var2
small_var2<-delete.vertices(var2, lowerthanmedian.network_var2)
#Plotting based only on degree measures
edge.start <- ends(small_var2, es=E(small_var2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(small_var2))
maxC <- rep(Inf, vcount(small_var2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(small_var2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(small_var2)$weight)
#PLotting
plot(small_var2,
layout=co,
edge.color=V(small_var2)$color[edge.start],
edge.arrow.size=(degree(small_var2)+1)/1000,
edge.width=E(small_var2)$weight/10,
edge.curved = TRUE,
vertex.size=log((V(small_var2)$var2_centr_degree+2))*20,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(small_var2,"LABEL_COR"),
vertex.label.cex=log((degree(small_var2)+2))/3,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(small_var2)$LABEL_COR
b<-V(small_var2)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=4,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Smaller Than Median Degree - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Mean In Degree: %.2f\nMean Out Degree: %.2f",
mean(degree(small_var2, mode="in")),
mean(degree(small_var2, mode="out"))
)
)
#Set Seed
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(var2_simplified, es=E(var2_simplified), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var2_simplified))
maxC <- rep(Inf, vcount(var2_simplified))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var2_simplified, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var2_simplified)$weight)
#Plotting based only on degree measures #var2_simplified_a.nn.deg
V(var2_simplified)$var2_a.nn.deg<-as.numeric(graph.knn(var2_simplified)$knn)
V(var2_simplified)$var2_a.nn.deg[V(var2_simplified)$var2_a.nn.deg=="NaN"]<-0
#PLotting
plot(var2_simplified,
layout=co,
edge.color=V(var2_simplified)$color[edge.start],
edge.arrow.size=sqrt((V(var2_simplified)$var2_a.nn.deg)^2+1)/1000,
edge.width=E(var2_simplified)$weight/100,
edge.curved = TRUE,
vertex.color=V(var2_simplified)$color,
vertex.size=(sqrt((V(var2_simplified)$var2_a.nn.deg)^2))/5,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var2_simplified,"LABEL_COR"),
vertex.label.cex=(sqrt((V(var2_simplified)$var2_a.nn.deg)^2)+1)/500,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var2_simplified)$LABEL_COR
b<-V(var2_simplified)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=4,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Average Neighbor Degree Sized - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median Average Neighbor Degree: %.2f",
median((var2_a.nn.deg+1))
))
#Set Seed
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(var2_simplified, es=E(var2_simplified), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(var2_simplified))
maxC <- rep(Inf, vcount(var2_simplified))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(var2_simplified, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(var2_simplified)$weight)
#Plotting based only on degree measures #var2_a.nn.deg
V(var2_simplified)$var2_a.nn.deg_w<-as.numeric(graph.knn(var2_simplified, weights = E(var2_simplified)$weight)$knn)
V(var2_simplified)$var2_a.nn.deg_w[V(var2_simplified)$var2_a.nn.deg_w=="NaN"]<-0
#PLotting
plot(var2_simplified,
layout=co,
edge.color=V(var2_simplified)$color[edge.start],
edge.arrow.size=sqrt((V(var2_simplified)$var2_a.nn.deg_w)^2+1)/1000,
edge.width=E(var2_simplified)$weight/100,
edge.curved = TRUE,
vertex.color=V(var2_simplified)$color,
vertex.size=(sqrt((V(var2_simplified)$var2_a.nn.deg_w)^2))/5,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(var2_simplified,"LABEL_COR"),
vertex.label.cex=(sqrt((V(var2_simplified)$var2_a.nn.deg_w)^2)+1)/500,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(var2_simplified)$LABEL_COR
b<-V(var2_simplified)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=4,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Average Weighted Neighbor Degree Sized - 4_REFERENCIA DE RECEBIMENTO CONTRAREFERENCIA (var2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median Average Weighted Neighbor Degree: %.2f",
median((var2_a.nn.deg_w+1))
))
#Circle Degree ***Too intense computation***
#A_var2 <- get.adjacency(var2, sparse=FALSE)
#detach("package:igraph", unload=TRUE)
#library(network)
#g <- network::as.network.matrix(A_var2)
#library(sna)
#gplot.target(g, degree(g), main="Circle Degree")
#library(igraph)
save.image("~/SNArRDJF/Robject/var2_data.RData")